This project is an AI-powered document intelligence platform that allows users to upload documents, ask natural language questions, and receive context-aware answers generated using Retrieval-Augmented Generation (RAG).
The system combines document parsing, embeddings, vector search, and large language models to help users interact with long and complex documents more efficiently.
Unlike traditional keyword-based search systems, the platform understands semantic meaning and retrieves the most relevant document chunks before generating a final response.
Organizations often work with large volumes of PDFs, reports, manuals, legal documents, research papers, and internal knowledge bases.
Key challenges include:
- Manually searching through long documents
- Missing important information hidden in large text blocks
- Difficulty finding contextually relevant answers
- Time-consuming document review processes
- Lack of intelligent search across multiple files
This project demonstrates how AI can:
- Convert documents into searchable knowledge bases
- Retrieve relevant context using embeddings and vector search
- Generate accurate answers using LLMs
- Improve productivity and reduce manual effort
| Dimension | Basic RAG Chatbot | This Project |
|---|---|---|
| Retrieval Scope | Searches across all files together | Supports scoped document retrieval |
| Hallucination Prevention | Limited | Uses context-constrained prompting |
| Transparency | Final answer only | Returns answer with supporting context |
| Explainability | Minimal | Shows which document sections influenced output |
| Document Handling | Basic upload | Multi-document processing and semantic search |
| Evaluation | Usually absent | Supports response evaluation and validation |
- Project Type: AI-Powered Document Intelligence System
- Core Approach: Retrieval-Augmented Generation (RAG)
- Frontend: React + TypeScript + Shadcn UI
- Backend: FastAPI
- LLM Provider: Groq
- Embeddings: HuggingFace Sentence Transformers
- Vector Store: ChromaDB
- Framework: LangChain
- Document Types: PDF, DOCX, TXT
User Uploads Document
↓
Document Parsing
↓
Text Chunking
↓
Embedding Generation
↓
ChromaDB Vector Storage
↓
User Query
↓
Semantic Retrieval
↓
Groq LLM Response Generation
↓
Final Context-Aware Answer
- Upload and process multiple documents
- Supports PDF, DOCX, and TXT files
- Converts raw files into searchable text
- Breaks large documents into smaller chunks
- Preserves contextual meaning for better retrieval
- Enables efficient embedding generation and search
- Uses HuggingFace embeddings to represent document meaning
- Retrieves semantically relevant chunks instead of exact keyword matches
- Improves answer quality for complex queries
- Stores document embeddings in ChromaDB
- Enables fast similarity search and efficient retrieval
- Supports scalable document indexing workflows
- Retrieves the most relevant document chunks
- Sends contextual information to the LLM
- Produces grounded and context-aware answers
- Displays which document sections contributed to the answer
- Improves transparency and trust in generated responses
- Helps users validate the output against the source material
- Responsive React frontend with clean UI
- FastAPI backend for document processing and API handling
- Interactive workflow for uploading, querying, and reviewing documents
| Endpoint | Method | Purpose |
|---|---|---|
/api/upload |
POST | Upload and process document files |
/api/query |
POST | Ask questions against uploaded documents |
/api/list_documents |
GET | Retrieve list of indexed documents |
- User uploads a financial report PDF
- The document is parsed and converted into chunks
- Each chunk is embedded and stored in ChromaDB
- User asks: “What was the net revenue in Q3?”
- The system retrieves the most relevant chunks
- The LLM generates an answer only from retrieved evidence
- Answer: “Q3 net revenue increased by 12% year-over-year.”
- Supporting document sections are also displayed to improve trust and explainability
The project uses document-aware retrieval to avoid mixing unrelated document content. This improves answer accuracy and makes the platform more reliable for multi-document use cases.
The LLM is instructed to answer only from retrieved content. If no relevant information exists, the system returns a safe fallback response instead of generating unsupported answers.
The backend is organized into separate services and routes for upload, query, and document listing. This makes the system easier to maintain and extend.
Configuration values such as API keys, chunk size, CORS settings, and frontend URLs are stored separately to support local development and deployment flexibility.
- Evidence-backed answers using retrieved document chunks
- Retrieval transparency for better explainability
- Graceful fallback when no relevant information is found
- Document-level context control for improved accuracy
- Reduced hallucination risk through grounded prompting
- Python
- FastAPI
- React
- TypeScript
- Shadcn UI
- LangChain
- ChromaDB
- HuggingFace Embeddings
- Groq API
- Tailwind CSS
rag-document-intelligence-platform/
│
├── README.md
├── .gitignore
│
├── backend/
│ ├── main.py
│ ├── config.py
│ ├── Procfile
│ ├── requirements.txt
│ ├── ReadMe.MD
│ ├── routes/
│ │ ├── upload.py
│ │ ├── query.py
│ │ ├── retrieval.py
│ │ └── list_documents.py
│ └── services/
│ ├── embedding_service.py
│ ├── llm_service.py
│ ├── pdf_processor.py
│ └── vector_store.py
│
├── ui-v1/
│ ├── README.md
│ ├── package.json
│ ├── package-lock.json
│ ├── bun.lockb
│ ├── components.json
│ ├── eslint.config.js
│ ├── index.html
│ ├── postcss.config.js
│ ├── tailwind.config.ts
│ ├── vite.config.ts
│ ├── tsconfig.app.json
│ ├── tsconfig.json
│ ├── tsconfig.node.json
│ ├── public/
│ └── src/
│ ├── App.css
│ ├── App.tsx
│ ├── index.css
│ ├── main.tsx
│ ├── components/
│ ├── context/
│ ├── hooks/
│ ├── lib/
│ ├── pages/
│ └── utils/
│
└── images/
├── platform-home.png
├── query-interface.png
├── explainable-results.png
└── trustworthy-ai-output.png
cd backend
pip install -r requirements.txt
uvicorn main:app --reloadcd ui-v1
npm install
npm run devOPENAI_API_KEY=your_api_key
PINECONE_API_KEY=your_api_key
PINECONE_INDEX=your_index_name
FRONTEND_URL=http://localhost:5173
- Reduces manual document search effort
- Improves speed of finding relevant information
- Enables natural language interaction with documents
- Supports semantic understanding rather than exact matching
- Creates a more efficient knowledge retrieval workflow
- Research paper summarization
- Legal document analysis
- HR policy search assistant
- Internal company knowledge base
- Contract review support
- Financial report analysis
- Healthcare documentation lookup
- Saves time spent searching through large documents
- Improves employee productivity
- Reduces knowledge access bottlenecks
- Enables faster decision-making
- Makes internal information more accessible
- Response quality depends on document quality
- Large documents may increase embedding time
- LLM answers may still require human verification
- Performance depends on chunking strategy and retrieval quality
- Add support for image-based PDFs using OCR
- Add document summarization functionality
- Support conversation memory across sessions
- Add role-based access control
- Deploy using Docker and cloud infrastructure
- Add citation highlighting for retrieved answers
- Support multi-language document processing
MIT License
Built by Sucharitha Reddy Gaddam
This project demonstrates how Retrieval-Augmented Generation can transform traditional document search into an intelligent AI-powered experience.
By combining FastAPI, React, ChromaDB, LangChain, HuggingFace embeddings, and Groq LLMs, the project provides a practical example of how businesses can build scalable document intelligence systems.
The project highlights strong skills in AI engineering, full-stack development, vector databases, semantic search, and LLM application design, making it highly relevant for AI Engineer, ML Engineer, and Full Stack Developer roles.